Triple
T2781766
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helvering v. Clifford |
E61712
|
entity |
| Predicate | isLandmarkFor |
P43158
|
FINISHED |
| Object | U.S. federal income tax law |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: U.S. federal income tax law | Statement: [Helvering v. Clifford, isLandmarkFor, U.S. federal income tax law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isLandmarkFor Context triple: [Helvering v. Clifford, isLandmarkFor, U.S. federal income tax law]
-
A.
includesLandmark
Indicates that one location or area contains or encompasses a specific landmark within its boundaries.
-
B.
isLocalLandmark
Indicates that something is recognized as a notable or significant landmark within a specific local area or community.
-
C.
isSiteOf
Indicates that a location or place serves as the setting or host for a particular event, activity, or feature.
-
D.
cityLandmarkID
Indicates that a specific landmark is uniquely identified as being located within a particular city.
-
E.
hasLandmarkArea
Indicates that a specified area is designated as the landmark area associated with a particular entity or location.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ab4b7e43c48190997b8fc8fb1663ab |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abddceb9d88190961e30d521a21552 |
completed | March 7, 2026, 8:11 a.m. |
| PD | Predicate disambiguation | batch_69abdd00b65c8190a8ea444308c4fa2b |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abddcc348081908b5f760899389d4f |
completed | March 7, 2026, 8:11 a.m. |
Created at: March 6, 2026, 9:57 p.m.